
where \(\hat{S}\)  is row-normalized, with features projected to the unit sphere. This loss encourages orthogonality between features from different classes. Similar to [1]}, we downscale this loss by a factor of \(0.03\) . We use \(L_{\rm aux}\)  as the auxiliary loss in our subspace network implementation [1]}, and it replaces the SimCLR episodes in our CrossTransformer implementation [3]}. We include it in our own model for consistency, and include an ablation study in Sec. REF .
